Data was available in a .csv format. In the original data file, one column was about the country, one was of Type, and the rest of the columns were the years from 1960 to 2020. However, we changed the columns and rows so that they can be used easily. Therefore, in rows there was one column for country, one column for Year and the rest 16 columns consisted of different varieties of information for different countries for all years. It comprises percent of primary education of females and males over the years, data of primary pupils overall, and females separately. Data also reflected the number of overall teachers in the primary education sector over the Year for all countries. This data is subdivided into the number of primary teachers for males and females. Further, it also reflected the percentage of trained teachers in the primary and pre-primary sector; categorized into male, female, and overall.
Moreover, it also showed the net enrolment of female v/s male teachers in primary education and the total gross intake of males and females for different countries. Each cell would give us the value corresponding to the particular category for a given year and country. However, the data was not available for all years and categories for every country. Therefore, we did not include the years from 1960 to 2000. For expenditure we had a separate sheet in which we took years from 2000 to 2020 and included only those countries whose data was available for at least 15 years. We can fill the NAN value with a more appropriate value like statistical mean/median or some constant or we can drop it with dropna(). The values were filled using the mean value of all years in the expenditure sheet. NAN values can be filled with fillna() or apply function in python.
from IPython.display import HTML
HTML('''<button type="button" class="btn btn-outline-danger" onclick="codeToggle();">Toggle Code</button>''')
{
"tags": [
"hide-input",
]
}
#2000-2020
#x-axis -> %male
#y-axis -> %female
#size -> max(total teachers)
#Animation -> year
from plotly.offline import plot, iplot, init_notebook_mode
init_notebook_mode(connected=True)
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import plotly.express as px
#from google.colab import files
import plotly.graph_objects as go
df = pd.read_csv('./data/Data_final.csv')
mean_value=df['Primary Trained Male'].mean()
df['Primary Trained Male'].fillna(value=mean_value, inplace=True)
mean_value=df['Primary Trained Female'].mean()
df['Primary Trained Female'].fillna(value=mean_value, inplace=True)
x = {*()}
for i in range(16226):
y = df.iloc[i]['Country Name']
x.add(y)
country_name = list(x)
year = []
for i in range(2000,2021):
year.append(i)
for y in range(2000,2021):
x_arr = []
y_arr = []
ratio = []
for i in range(16226):
if(df.iloc[i]['Year'] == y):
if(df.iloc[i]['Primary Trained Male']!=0):
x_arr.append(df.iloc[i]['Primary Trained Male'])
y_arr.append(df.iloc[i]['Primary Trained Female'])
ratio.append(df.iloc[i]['Primary Trained Female']/df.iloc[i]['Primary Trained Male'])
else:
x_arr.append(df.iloc[i]['Primary Trained Male'])
y_arr.append(df.iloc[i]['Primary Trained Female'])
ratio.append(0)
fig = px.scatter(df, x=x_arr, y=y_arr,size=ratio, hover_data=[country_name], color = country_name, title = "Female Trained Teachers / Male Trained Teachers in different countries"+str(" ")+str( y), labels=dict(x="Primary Trained Male (%)", y="Primary Trained Female (%)", color="Place"))
fig.show()
# print(ratio)
# new_ratio = np.array([x*2000 for x in ratio])
The above graph is plotted Year wise between the percentage of Primary Trained Male teachers and the percentage of Primary Trained Female teachers. The different colored circles display different countries. However, since we do not have much data for a few countries, we have some of the bubbles as outliers for which we cannot bring out any conclusion. We used the fillna() function to fill NaN values with the mean of the column. Overall in the graph we can see that from the year 2000 to 2020 both the percentage of Trained Male and Trained Female teachers have increased. Many countries during the year 2000 had several Trained Male and Trained Female Teachers nearly less than 50 percent. However, By 2020, most countries reached nearly to a hundred percent of trained Male and Female primary teachers. We can see that in 2000 countries like Zambia had significantly less percent( < 45% both) of both female and male trained teachers still maintaining the ratio to nearly 1.4. While, countries like Austria, the Philippines and Gabon have almost 100% trained teachers with a ratio of approximately 1. IN 2005 Bangladesh showed a drastic increase in female trained teachers than male trained teachers. In recent years countries like Lucia and many western African countries have shown a relative decrease in trained teachers for males compared to females.
Bahrain recently 2020 reported having 100% Trained Teachers for primary education. This data remained consistent with the previous Year's numbers, i.e., 2019. In Bahrain, the average Trained teacher from 2011 to 2020 is around 83%. In 2011 Bahrain reported that around 80% of the trained teachers in both the Male and Female categories. From 2011 to 2017, the ratio of trained teachers in primary education increased slowly. Whereas, from 2017 to 2018, it was a sudden increase. The data shows that the percentage of trained female teachers in Bahrain is slightly more significant than that of trained male teachers. There is no gender discrimination in Bahrain as per their constitution formed in 2002. The result of having more trained female teachers than males shows the openness of the country Bahrain. Another reason for the increased trained teacher can be due to the internet. With the help of the internet, more and more teachers in Bahrain can become trained professionals.
Caribbean countries are a few countries that lag in terms of primary education. However, the number of Trained teachers in females has a significantly higher percentage than Male trained teachers and this is true after the fact that most of the Teachers in Caribbean countries are Female. Over the years, Trained female teachers have gone from 77% to around 88% whereas the number of trained male teachers increased by only 4 percent. This is seen because of low-level remuneration, many of the trained teachers leave the profession to take up more lucrative posts in the private sector or migrate to countries like the Bahamas.
Ghana in particular has seen a relative decrease in the percentage of trained teachers in both males and females. More particularly, in females the percentage decreased from 88% to 77%. The explanation for this is that the percentage of female teachers gradually increased relatively. As more instructors arrived, it became increasingly difficult to train them. Also, Ghana is a country suffering from gender discrimination, females are not allowed many freedoms. Therefore, females are kept untrained. However, the percentage of trained male teachers initially decreased because more and more males were becoming teachers; it was challenging for them to be trained. However, the percentage of trained male teachers remained the same in 2020 as in 2000.
{
"tags": [
"hide-input",
]
}
# Net Enrollment Rate
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
iris_df = pd.read_csv("./data/Data_final (1).csv", index_col = "Year")
# mean_value = iris_df['Net Enrollment Male'].mean()
# iris_df['Net Enrollment Male'].fillna(value=mean_value, inplace=True)
# mean_value = iris_df['Net Enrollment Female'].mean()
# iris_df['Net Enrollment Female'].fillna(value=mean_value, inplace=True)
# mean_value = iris_df['Net Enrollment'].mean()
# iris_df['Net Enrollment'].fillna(value=mean_value, inplace=True)
arr = [i for i in range(1960,2000)]
iris_df.drop(arr, axis=0, inplace=True)
r = ["Net Enrollment","Net Enrollment Female","Net Enrollment Male"]
iris_df = iris_df.dropna(subset = r)
# print(iris_df.shape)
# ratio = []
# for i in range(2138):
# ratio.append(iris_df.iloc[i]['Net Enrollment Female']/iris_df.iloc[i]['Net Enrollment Male'])
# # print(ratio)
# iris_df['ratio'] = ratio
# print(iris_df.ratio)
# r = ["Net Enrollment","Net Enrollment Female","Net Enrollment Male"]
# iris_df = iris_df.dropna(subset = r)
# print(iris_df.head)
iris_df['Year1'] = iris_df.index
# df['Year1']
# print(iris_df.Year1)
# display(iris_df.Year1.to_string())
# iris_df['Net Enrollment'] = iris_df['Net Enrollment']/1000
# female_pow = []
# for i in range(2138):
# female_pow.append( (iris_df.iloc[i]['Net Enrollment Female'])**3 )
# iris_df['female_pow'] = female_pow
iris_df = iris_df.sort_values('Year1', ascending=True)
fig = px.scatter_geo(iris_df, locations="Country Code", size="Net Enrollment Female", size_max=10, animation_group = "Country Name",
projection="natural earth",color="Country Name", animation_frame="Year1", template="plotly_dark")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
This shows the percentage of female enrollment across various countries. The size of the circles shows the relative percentage of females. If the size is more significant, that means that the percentage of female enrollment in that particular country is greater than that of other countries. In the Western Countries, we can see that the relative percentage of female enrollment in primary education is lower than in most other countries. This is due to West Africa having different cultures and having gender discrimination, riots based on color, caste. People in Western Africa do not feel it appropriate to educate women. However, we can see that the percentage of female enrollment has increased in those countries over the past few years. This is due to more awareness of gender equality and women empowerment. We can also see that India had an initial percentage of female enrollment of around 70 during 2000. However, by 2012 women's empowerment and gender equality turned the percentage to near 90%. Similarly, our neighboring country Bhutan had 48% female enrollment however by 2018 the percentage of female enrollment reached 90%.
This data shows how the awareness of gender equality has changed the world. It clearly shows how the percentage of female enrollment in primary education has increased. These changes are due to modernization. Hence, women's empowerment has improved the ratio of females in education. As we know, education is most important. Therefore, educating more females can change the world regarding women's empowerment.
{
"tags": [
"hide-input",
]
}
#pie
# Trained teacher vs Total teacher
# Primary Trained vs Primary Education Teacher (number)
import plotly.express as px
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from IPython.display import HTML
#from google.colab import files
df = pd.read_csv("./data/Data_final (1).csv")
# print(df.head())
arr = [i for i in range(1960,2000)]
df.drop(arr, axis=0, inplace=True)
r = ["Primary Trained","Primary Education Teacher"]
df = df.dropna(subset = r)
# print(df.shape)
Train_no = []
for i in range(1047):
Train_no.append(( (df.iloc[i]["Primary Trained"])*(df.iloc[i]["Primary Education Teacher"]) )/100)
df['Train_no'] = Train_no
sum_train = []
for y in range(2000,2021):
sum_a = []
for i in range(1047):
if(df.iloc[i]['Year'] == y):
sum_a.append(df.iloc[i]['Train_no'])
sum_train.append(sum(sum_a))
sum_total = []
for y in range(2000,2021):
sum_a = []
for i in range(1047):
if(df.iloc[i]['Year'] == y):
sum_a.append(df.iloc[i]['Primary Education Teacher'])
sum_total.append(sum(sum_a))
# print(sum_train)
# print(sum_total)
pie_percent = []
for i in range(21):
pie_percent.append((sum_train[i]/sum_total[i])*100)
#change values of i in pie_percent[i] and 100-pie_percent[i] to get different pie graphs of repective year
for i in range(21):
y = np.array( [pie_percent[i],100-pie_percent[i]])
mylabels = ["Trained", "UnTrained"]
fig, ax = plt.subplots(figsize = (5,4))
fig.set_facecolor('white')
explode = (0.1, 0)
plt.pie(y, labels = mylabels,autopct='%1.1f%%', explode = explode, shadow=True)
plt.title("World Trained Teacher vs World Non-Trained Teacher"+str(" ")+ str(i+2000))
# plt.savefig(str(i+2000)+".png")
plt.show()
# HTML("""
# <video width="300" height="200" controls src="./assets/Pie_chart.mp4">
# </video>
# """)
Each pie chart shows the year-wise percentage value of trained and untrained primary teachers worldwide. For calculation of this part we first added all the primary teachers Year-wise, and then added all the untrained teachers Year wise. Then, we calculated the percentage of untrained primary teachers. The remaining percentage conveys the percentage of trained teachers.
In 2000 total untrained teachers all over the world were about 17%. However, in 2011 the number of untrained teachers was 27%. The vast majority of the trained teachers were changing professions around those times. The primary teaching profession was a low self-esteem job. Therefore, all the trained professionals were leaving their jobs and were applying for higher esteem jobs with higher salaries. This gradually increased the percentage of untrained teachers. In 2011, more people became aware of primary education in a country. Therefore, more and more teachers were being trained and people were again interested in the teaching profession. Recognizing the significance of elementary education, every country strives to recruit more teachers by offering competitive wages and preparing them through competitive tests. Therefore, the pie chart shows a gradual decrease in the percentage of untrained teachers. Also, we see a 7% decrease in the percentage of untrained teachers from the Year 2019 to 2020. The reason being, due to covid we shifted from offline mode to online mode. Due to having an option for online training, it was easier to train more teachers, which helped increase the number of trained teachers. Overall, from 2000 to 2020 we see a net decrease in untrained teachers. Therefore, this gives a positive impression about the overall world moving toward modernization and education.
import pandas as pd
import bar_chart_race as bcr
# import pandas as pd
# from matplotlib import pyplot as plt
# # Read CSV into pandas
# data = pd.read_csv(r"expen_final.csv")
# data.head()
# df = pd.DataFrame(data)
# name = df['car'].head(12)
# price = df['price'].head(12)
# # Figure Size
# fig = plt.figure(figsize =(10, 7))
# # Horizontal Bar Plot
# plt.bar(name[0:10], price[0:10])
# # Show Plot
# plt.show()
df=pd.read_csv("./data/expen_final.csv", index_col = "Year")
# df.head()
# df.isnull().sum(axis =0)
#perc = 75.0
#min_count = int(((100-perc)/100)*df.shape[0] + 1)
#df = df.dropna( axis=1,
# thresh=min_count)
ls = list(df)
g = len(ls)
for i in range(g):
var = df[ls[i]].isna().sum()
#var = df[ls[i]]
if(var > 6):
#df.drop(ls[i], axis =1)
del df[ls[i]]
l = list(df)
x=len(l)
for i in range(1,x):
m = df[l[i]].mean()
df[l[i]].fillna(value = m, inplace = True)
bcr.bar_chart_race(df=df, sort='desc', steps_per_period=3, title="Expenditure of countires in different years",figsize=(5, 9), n_bars = 60)
After processing Nan values and plotting we can see that the expenditure on primary education out of total expenditure on education is more for under-developed countries. Whereas the developed countries have less expenditure on primary education out of total expenditure on education. For e.g. Finland is a country with the highest literacy rate, it spends less on primary education than on higher education. Whereas the region of West Africa spends more than 50% of its total expenditure on primary education. Because under-developed and developing countries require more focus on primary education since it is an essential investment in developing a country.
Similarly, for South Africa, we can see that the percentage spent on primary education is more significant from 2000 to 2012. Since then, several countries in South Africa have grown to the point where the emphasis has shifted from primary education to higher education.
OVERALL CONCLUSION
The overall conclusion is that the importance of primary education is taken seriously by every country. Primary education is an essential investment for all developing countries and developed countries